Data Science has drenched every domain – from finance and education to healthcare, social research and so on. Without data literacy, there cannot be any concrete analysis and finding. But if understanding and analysing data is only restricted to professional data scientists, wouldn’t the process be too restrictive? New-age corporates have realised the importance of Data Science democratization and spreading data literacy across levels.

Data Science Literacy: Is it only subjective to Data Scientists?

To track tax defaulters, the income tax department studies anomalies in revenue-collection data. An educational institute, to measure and improve its academic success, analyses the pattern of passing, demographic of the students, attendance record, performance in classroom etc.

Hospitals collects pattern of disease occurrence, number of patient’s turnout every year, the statistics of medicine effectiveness etc, to be better prepared and serve their patients well. A sociologist studies the views and text of the people through exchanged through the social media and offline modes to study the evolving culture of a generation in the country.

Why should Data Science be democratized?

Confining most of the data knowledge to just a few people within a company is problematic on many levels. It is a restrictive practise and creates pressure on the countable number of Data Scientists. Not just that, the Data Scientists face a lot of difficulty in communicating their findings and answers to other teams which lack data literacy.

At the other end of the spectrum, business stakeholders are not satisfied with the pace at which their data requests are answered. Many a times, the stakeholders who aren’t familiar with data, fail to even frame the proper questions. Hence, we can see that not lack of data literacy has a cascading impact throughout the value chain.

A data-literate team is empowered to place better requests. A basic level of understanding of tools and resources of data goes a long way in improving the quality of interaction across departments and teams. When the number of iterations for each request reduces, it greatly enhances the speed and quality of the work at hand.

How to enhance data literacy in an organisation?

With a barrage of data, it is imperative that every employee in the organisation across levels is data literate and develops a habit of data-driven problem-solving. Even companies need to make conscious efforts so that Data Science expertise must be dispersed throughout an organisation.

Companies that want to achieve an edge over others through widespread data literacy can achieve it through three simple steps. (1) Creating awareness share data tools, (2) Imparting data skills, and (3) Inculcating a sense of data responsibility.

(1) Awareness and sharing of data tools: 

Once more employees are aware about the practical implementation of various data tools, basic data requests can be fulfilled by any trained employee. This way, expert data scientists can be relieved for more complex and important tasks. There can be a knowledge sharing portal wherein the experts can post a recent problem they have solved and how. This way others can learn the use of data and develop interest in exploring data science. It can bring about a sense of ownership amongst employees. This kind of knowledge sharing can also be incentivised.

Companies can also have online data university wherein free courses on Data Science, Big Data, and Artificial Intelligence. A community of data enthusiasts can be created who can exchange their learning and experiences. Some companies can also start “data boot camps” for new hires, where they can be introduced to SQL, basic business intelligence software or other analytical/ statistics concepts.

(2) Sharing skills through mentor-ship:

The more experienced Data Scientists can do hand-holding and encourage other colleagues to learn the ropes of Data Science. The new learners will pick up the skills quickly if the training is more specific to their department.  Centralized data team can build tools that enable everyone to do their data work faster. Involving concerned departments in the projects, analyzing the task and skills gaps in your company’s projects and mapping the skill sets of the internal staff accordingly are few of the steps that can be taken to spread the data skills.

(3) Inculcating a sense of responsibility:

Roles and responsibilities can be re-oriented and redefined in the wake of data-literacy. Each department and team should be able to access and understand the data sets most relevant to their own functions. This will not only bring in a sense of responsibility but also help then to understand the problem better and develop a relationship with the numbers.

Data literacy across existing verticals is the best method of optimal resource utilization 

It is well-known that finding experienced Data Science professionals can be a challenge. There is a massive skill gap when it comes to Data Science. Most companies have realized that instead of seeking professionals externally, training existing employees who have an aptitude for data-driven work is a smart strategy to plug skill gaps.

When the training is started in the early stages, the employees can be proficient in the basic knowledge. Empowering employees with these basic and necessary data skills can lead to enhanced innovation and efficiency in organisations. Achieving innovation is difficult through external consultants or only a handful of professional data scientists. When everyone working in the company will be data-literate, they can understand the business problems and requirements better. This directly reflects in the team’s performance as well as the company as a whole.